import pandas as pd
import numpy as np
IDH_2017 = pd.read_excel("./IDH_PNUD_2017.xlsx", sheet_name="Distrital")
### Eliminamos las filas
IDH_2017 = IDH_2017.drop([0, 1, 2, 3, 4, 5, 1880, 1881, 1882])
IDH_2017
| Índice de Desarrollo Humano distrital, 2017 | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 | Unnamed: 6 | Unnamed: 7 | Unnamed: 8 | Unnamed: 9 | Unnamed: 10 | Unnamed: 11 | Unnamed: 12 | Unnamed: 13 | Unnamed: 14 | Unnamed: 15 | Unnamed: 16 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6 | 010101 | 1 | Chachapoyas | NaN | NaN | 32589 | 169 | 0.605711 | 169 | 73.297544 | 1082 | 74.699578 | 182 | 9.7582 | 110 | 1086.762736 | 158 |
| 7 | 010102 | 2 | Asuncion | NaN | NaN | 262 | 1860 | 0.421303 | 762 | 72.349067 | 1172 | 66.666667 | 473 | 5.8662 | 943 | 569.610318 | 853 |
| 8 | 010103 | 3 | Balsas | NaN | NaN | 1136 | 1525 | 0.294232 | 1437 | 68.710568 | 1446 | 30.769231 | 1716 | 4.9866 | 1273 | 362.999904 | 1296 |
| 9 | 010104 | 4 | Cheto | NaN | NaN | 642 | 1721 | 0.329549 | 1228 | 83.896326 | 152 | 49.019608 | 1177 | 4.2735 | 1561 | 342.569379 | 1361 |
| 10 | 010105 | 5 | Chiliquin | NaN | NaN | 585 | 1749 | 0.264602 | 1592 | 77.261089 | 684 | 34.615385 | 1612 | 3.304 | 1789 | 308.814375 | 1439 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1875 | 250302 | 2 | Irazola | NaN | NaN | 10214 | 455 | 0.378183 | 959 | 85.697522 | 85 | 39.927624 | 1471 | 5.1274 | 1219 | 465.880288 | 1059 |
| 1876 | 250303 | 3 | Curimana | NaN | NaN | 7722 | 572 | 0.378119 | 961 | 85.109174 | 99 | 40.219378 | 1458 | 5.5175 | 1074 | 444.932792 | 1111 |
| 1877 | 250304 | 4 | Neshuya | NaN | NaN | 7594 | 576 | 0.437379 | 695 | 82.296773 | 229 | 45.68082 | 1302 | 6.1251 | 868 | 614.015288 | 766 |
| 1878 | 250305 | 5 | Alexander Von Humboldt | NaN | NaN | 5137 | 775 | 0.423282 | 753 | 83.636475 | 164 | 42.929293 | 1393 | 5.8305 | 957 | 582.998041 | 818 |
| 1879 | 250401 | 1 | Purus | NaN | NaN | 2860 | 1080 | 0.335455 | 1193 | 66.458846 | 1555 | 20.197044 | 1848 | 7.2707 | 537 | 517.759563 | 941 |
1874 rows × 17 columns
#Eliminamos las columnas
IDH_2017.drop(columns = ['Unnamed: 1', 'Unnamed: 3', 'Unnamed: 4'], inplace = True)
IDH_2017.head(5)
| Índice de Desarrollo Humano distrital, 2017 | Unnamed: 2 | Unnamed: 5 | Unnamed: 6 | Unnamed: 7 | Unnamed: 8 | Unnamed: 9 | Unnamed: 10 | Unnamed: 11 | Unnamed: 12 | Unnamed: 13 | Unnamed: 14 | Unnamed: 15 | Unnamed: 16 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6 | 010101 | Chachapoyas | 32589 | 169 | 0.605711 | 169 | 73.297544 | 1082 | 74.699578 | 182 | 9.7582 | 110 | 1086.762736 | 158 |
| 7 | 010102 | Asuncion | 262 | 1860 | 0.421303 | 762 | 72.349067 | 1172 | 66.666667 | 473 | 5.8662 | 943 | 569.610318 | 853 |
| 8 | 010103 | Balsas | 1136 | 1525 | 0.294232 | 1437 | 68.710568 | 1446 | 30.769231 | 1716 | 4.9866 | 1273 | 362.999904 | 1296 |
| 9 | 010104 | Cheto | 642 | 1721 | 0.329549 | 1228 | 83.896326 | 152 | 49.019608 | 1177 | 4.2735 | 1561 | 342.569379 | 1361 |
| 10 | 010105 | Chiliquin | 585 | 1749 | 0.264602 | 1592 | 77.261089 | 684 | 34.615385 | 1612 | 3.304 | 1789 | 308.814375 | 1439 |
#Cambiamos los encabezados
nuevos_encabezados = ['ubigeo', 'distrito', 'pob_habitantes', 'pob_ranking', 'idh_coef', 'idh_ranking', 'esp_vida_años', 'esp_vida_ranking', 'educ_sec_%', 'educ_sec_ranking', 'educ_años', 'educ_ranking', 'ingreso_percap_mes', 'ingreso_percap_ranking']
IDH_2017 = IDH_2017.rename(columns=dict(zip(IDH_2017.columns, nuevos_encabezados)))
IDH_2017
| ubigeo | distrito | pob_habitantes | pob_ranking | idh_coef | idh_ranking | esp_vida_años | esp_vida_ranking | educ_sec_% | educ_sec_ranking | educ_años | educ_ranking | ingreso_percap_mes | ingreso_percap_ranking | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6 | 010101 | Chachapoyas | 32589 | 169 | 0.605711 | 169 | 73.297544 | 1082 | 74.699578 | 182 | 9.7582 | 110 | 1086.762736 | 158 |
| 7 | 010102 | Asuncion | 262 | 1860 | 0.421303 | 762 | 72.349067 | 1172 | 66.666667 | 473 | 5.8662 | 943 | 569.610318 | 853 |
| 8 | 010103 | Balsas | 1136 | 1525 | 0.294232 | 1437 | 68.710568 | 1446 | 30.769231 | 1716 | 4.9866 | 1273 | 362.999904 | 1296 |
| 9 | 010104 | Cheto | 642 | 1721 | 0.329549 | 1228 | 83.896326 | 152 | 49.019608 | 1177 | 4.2735 | 1561 | 342.569379 | 1361 |
| 10 | 010105 | Chiliquin | 585 | 1749 | 0.264602 | 1592 | 77.261089 | 684 | 34.615385 | 1612 | 3.304 | 1789 | 308.814375 | 1439 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1875 | 250302 | Irazola | 10214 | 455 | 0.378183 | 959 | 85.697522 | 85 | 39.927624 | 1471 | 5.1274 | 1219 | 465.880288 | 1059 |
| 1876 | 250303 | Curimana | 7722 | 572 | 0.378119 | 961 | 85.109174 | 99 | 40.219378 | 1458 | 5.5175 | 1074 | 444.932792 | 1111 |
| 1877 | 250304 | Neshuya | 7594 | 576 | 0.437379 | 695 | 82.296773 | 229 | 45.68082 | 1302 | 6.1251 | 868 | 614.015288 | 766 |
| 1878 | 250305 | Alexander Von Humboldt | 5137 | 775 | 0.423282 | 753 | 83.636475 | 164 | 42.929293 | 1393 | 5.8305 | 957 | 582.998041 | 818 |
| 1879 | 250401 | Purus | 2860 | 1080 | 0.335455 | 1193 | 66.458846 | 1555 | 20.197044 | 1848 | 7.2707 | 537 | 517.759563 | 941 |
1874 rows × 14 columns
#Primero creamos una nueva columna con los dos primeros digitos de ubigeo
IDH_2017['depart']= IDH_2017['ubigeo'].str[:2]
IDH_2017
| ubigeo | distrito | pob_habitantes | pob_ranking | idh_coef | idh_ranking | esp_vida_años | esp_vida_ranking | educ_sec_% | educ_sec_ranking | educ_años | educ_ranking | ingreso_percap_mes | ingreso_percap_ranking | depart | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6 | 010101 | Chachapoyas | 32589 | 169 | 0.605711 | 169 | 73.297544 | 1082 | 74.699578 | 182 | 9.7582 | 110 | 1086.762736 | 158 | 01 |
| 7 | 010102 | Asuncion | 262 | 1860 | 0.421303 | 762 | 72.349067 | 1172 | 66.666667 | 473 | 5.8662 | 943 | 569.610318 | 853 | 01 |
| 8 | 010103 | Balsas | 1136 | 1525 | 0.294232 | 1437 | 68.710568 | 1446 | 30.769231 | 1716 | 4.9866 | 1273 | 362.999904 | 1296 | 01 |
| 9 | 010104 | Cheto | 642 | 1721 | 0.329549 | 1228 | 83.896326 | 152 | 49.019608 | 1177 | 4.2735 | 1561 | 342.569379 | 1361 | 01 |
| 10 | 010105 | Chiliquin | 585 | 1749 | 0.264602 | 1592 | 77.261089 | 684 | 34.615385 | 1612 | 3.304 | 1789 | 308.814375 | 1439 | 01 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1875 | 250302 | Irazola | 10214 | 455 | 0.378183 | 959 | 85.697522 | 85 | 39.927624 | 1471 | 5.1274 | 1219 | 465.880288 | 1059 | 25 |
| 1876 | 250303 | Curimana | 7722 | 572 | 0.378119 | 961 | 85.109174 | 99 | 40.219378 | 1458 | 5.5175 | 1074 | 444.932792 | 1111 | 25 |
| 1877 | 250304 | Neshuya | 7594 | 576 | 0.437379 | 695 | 82.296773 | 229 | 45.68082 | 1302 | 6.1251 | 868 | 614.015288 | 766 | 25 |
| 1878 | 250305 | Alexander Von Humboldt | 5137 | 775 | 0.423282 | 753 | 83.636475 | 164 | 42.929293 | 1393 | 5.8305 | 957 | 582.998041 | 818 | 25 |
| 1879 | 250401 | Purus | 2860 | 1080 | 0.335455 | 1193 | 66.458846 | 1555 | 20.197044 | 1848 | 7.2707 | 537 | 517.759563 | 941 | 25 |
1874 rows × 15 columns
#Luego, reemplazamos los números por los nombres de los departamentos
IDH_2017.replace({'depart': {'01': 'Amazonas', '02': 'Ancash', '03': 'Apurímac', '04': 'Arequipa', '05': 'Ayacucho',
'06': 'Cajamarca', '07': 'Callao', '08': 'Cuzco', '09': 'Huancavelica', '10': 'Huánuco', '11': 'Ica',
'12': 'Junín', '13': 'La Libertad', '14': 'Lambayeque', '15': 'Lima', '16': 'Loreto',
'17': 'Madre de Dios', '18': 'Moquegua', '19': 'Pasco', '20': 'Piura', '21': 'Puno',
'22': 'San Martín', '23': 'Tacna', '24': 'Tumbes', '25': 'Ucayali'}}, inplace= True)
IDH_2017
| ubigeo | distrito | pob_habitantes | pob_ranking | idh_coef | idh_ranking | esp_vida_años | esp_vida_ranking | educ_sec_% | educ_sec_ranking | educ_años | educ_ranking | ingreso_percap_mes | ingreso_percap_ranking | depart | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6 | 010101 | Chachapoyas | 32589 | 169 | 0.605711 | 169 | 73.297544 | 1082 | 74.699578 | 182 | 9.7582 | 110 | 1086.762736 | 158 | Amazonas |
| 7 | 010102 | Asuncion | 262 | 1860 | 0.421303 | 762 | 72.349067 | 1172 | 66.666667 | 473 | 5.8662 | 943 | 569.610318 | 853 | Amazonas |
| 8 | 010103 | Balsas | 1136 | 1525 | 0.294232 | 1437 | 68.710568 | 1446 | 30.769231 | 1716 | 4.9866 | 1273 | 362.999904 | 1296 | Amazonas |
| 9 | 010104 | Cheto | 642 | 1721 | 0.329549 | 1228 | 83.896326 | 152 | 49.019608 | 1177 | 4.2735 | 1561 | 342.569379 | 1361 | Amazonas |
| 10 | 010105 | Chiliquin | 585 | 1749 | 0.264602 | 1592 | 77.261089 | 684 | 34.615385 | 1612 | 3.304 | 1789 | 308.814375 | 1439 | Amazonas |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1875 | 250302 | Irazola | 10214 | 455 | 0.378183 | 959 | 85.697522 | 85 | 39.927624 | 1471 | 5.1274 | 1219 | 465.880288 | 1059 | Ucayali |
| 1876 | 250303 | Curimana | 7722 | 572 | 0.378119 | 961 | 85.109174 | 99 | 40.219378 | 1458 | 5.5175 | 1074 | 444.932792 | 1111 | Ucayali |
| 1877 | 250304 | Neshuya | 7594 | 576 | 0.437379 | 695 | 82.296773 | 229 | 45.68082 | 1302 | 6.1251 | 868 | 614.015288 | 766 | Ucayali |
| 1878 | 250305 | Alexander Von Humboldt | 5137 | 775 | 0.423282 | 753 | 83.636475 | 164 | 42.929293 | 1393 | 5.8305 | 957 | 582.998041 | 818 | Ucayali |
| 1879 | 250401 | Purus | 2860 | 1080 | 0.335455 | 1193 | 66.458846 | 1555 | 20.197044 | 1848 | 7.2707 | 537 | 517.759563 | 941 | Ucayali |
1874 rows × 15 columns
#Ordenamos las columnas
IDH_2017 = IDH_2017.reindex(columns=[ "ubigeo", "depart", 'distrito', 'pob_habitantes', 'pob_ranking', 'idh_coef', 'idh_ranking', 'esp_vida_años', 'esp_vida_ranking', 'educ_sec_%', 'educ_sec_ranking', 'educ_años', 'educ_ranking', 'ingreso_percap_mes', 'ingreso_percap_ranking'])
IDH_2017
| ubigeo | depart | distrito | pob_habitantes | pob_ranking | idh_coef | idh_ranking | esp_vida_años | esp_vida_ranking | educ_sec_% | educ_sec_ranking | educ_años | educ_ranking | ingreso_percap_mes | ingreso_percap_ranking | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6 | 010101 | Amazonas | Chachapoyas | 32589 | 169 | 0.605711 | 169 | 73.297544 | 1082 | 74.699578 | 182 | 9.7582 | 110 | 1086.762736 | 158 |
| 7 | 010102 | Amazonas | Asuncion | 262 | 1860 | 0.421303 | 762 | 72.349067 | 1172 | 66.666667 | 473 | 5.8662 | 943 | 569.610318 | 853 |
| 8 | 010103 | Amazonas | Balsas | 1136 | 1525 | 0.294232 | 1437 | 68.710568 | 1446 | 30.769231 | 1716 | 4.9866 | 1273 | 362.999904 | 1296 |
| 9 | 010104 | Amazonas | Cheto | 642 | 1721 | 0.329549 | 1228 | 83.896326 | 152 | 49.019608 | 1177 | 4.2735 | 1561 | 342.569379 | 1361 |
| 10 | 010105 | Amazonas | Chiliquin | 585 | 1749 | 0.264602 | 1592 | 77.261089 | 684 | 34.615385 | 1612 | 3.304 | 1789 | 308.814375 | 1439 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1875 | 250302 | Ucayali | Irazola | 10214 | 455 | 0.378183 | 959 | 85.697522 | 85 | 39.927624 | 1471 | 5.1274 | 1219 | 465.880288 | 1059 |
| 1876 | 250303 | Ucayali | Curimana | 7722 | 572 | 0.378119 | 961 | 85.109174 | 99 | 40.219378 | 1458 | 5.5175 | 1074 | 444.932792 | 1111 |
| 1877 | 250304 | Ucayali | Neshuya | 7594 | 576 | 0.437379 | 695 | 82.296773 | 229 | 45.68082 | 1302 | 6.1251 | 868 | 614.015288 | 766 |
| 1878 | 250305 | Ucayali | Alexander Von Humboldt | 5137 | 775 | 0.423282 | 753 | 83.636475 | 164 | 42.929293 | 1393 | 5.8305 | 957 | 582.998041 | 818 |
| 1879 | 250401 | Ucayali | Purus | 2860 | 1080 | 0.335455 | 1193 | 66.458846 | 1555 | 20.197044 | 1848 | 7.2707 | 537 | 517.759563 | 941 |
1874 rows × 15 columns
#Primero creamos una nueva columna con los dos primeros digitos de ubigeo
IDH_2017['región']= IDH_2017['ubigeo'].str[:2]
IDH_2017
| ubigeo | depart | distrito | pob_habitantes | pob_ranking | idh_coef | idh_ranking | esp_vida_años | esp_vida_ranking | educ_sec_% | educ_sec_ranking | educ_años | educ_ranking | ingreso_percap_mes | ingreso_percap_ranking | región | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6 | 010101 | Amazonas | Chachapoyas | 32589 | 169 | 0.605711 | 169 | 73.297544 | 1082 | 74.699578 | 182 | 9.7582 | 110 | 1086.762736 | 158 | 01 |
| 7 | 010102 | Amazonas | Asuncion | 262 | 1860 | 0.421303 | 762 | 72.349067 | 1172 | 66.666667 | 473 | 5.8662 | 943 | 569.610318 | 853 | 01 |
| 8 | 010103 | Amazonas | Balsas | 1136 | 1525 | 0.294232 | 1437 | 68.710568 | 1446 | 30.769231 | 1716 | 4.9866 | 1273 | 362.999904 | 1296 | 01 |
| 9 | 010104 | Amazonas | Cheto | 642 | 1721 | 0.329549 | 1228 | 83.896326 | 152 | 49.019608 | 1177 | 4.2735 | 1561 | 342.569379 | 1361 | 01 |
| 10 | 010105 | Amazonas | Chiliquin | 585 | 1749 | 0.264602 | 1592 | 77.261089 | 684 | 34.615385 | 1612 | 3.304 | 1789 | 308.814375 | 1439 | 01 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1875 | 250302 | Ucayali | Irazola | 10214 | 455 | 0.378183 | 959 | 85.697522 | 85 | 39.927624 | 1471 | 5.1274 | 1219 | 465.880288 | 1059 | 25 |
| 1876 | 250303 | Ucayali | Curimana | 7722 | 572 | 0.378119 | 961 | 85.109174 | 99 | 40.219378 | 1458 | 5.5175 | 1074 | 444.932792 | 1111 | 25 |
| 1877 | 250304 | Ucayali | Neshuya | 7594 | 576 | 0.437379 | 695 | 82.296773 | 229 | 45.68082 | 1302 | 6.1251 | 868 | 614.015288 | 766 | 25 |
| 1878 | 250305 | Ucayali | Alexander Von Humboldt | 5137 | 775 | 0.423282 | 753 | 83.636475 | 164 | 42.929293 | 1393 | 5.8305 | 957 | 582.998041 | 818 | 25 |
| 1879 | 250401 | Ucayali | Purus | 2860 | 1080 | 0.335455 | 1193 | 66.458846 | 1555 | 20.197044 | 1848 | 7.2707 | 537 | 517.759563 | 941 | 25 |
1874 rows × 16 columns
#Luego, reemplazamos los números por los nombres de las regines
IDH_2017.replace({'región': {'01': 'Selva', '02': 'Costa', '03': 'Sierra', '04': 'Costa', '05': 'Sierra',
'06': 'Sierra', '07': 'Lima y Callao', '08': 'Sierra', '09': 'Sierra', '10': 'Sierra', '11': 'Costa',
'12': 'Sierra', '13': 'Costa', '14': 'Costa', '15': 'Lima y Callao', '16': 'Selva',
'17': 'Selva', '18': 'Sierra', '19': 'Sierra', '20': 'Costa', '21': 'Sierra',
'22': 'Selva', '23': 'Costa', '24': 'Costa', '25': 'Selva'}}, inplace= True)
IDH_2017
| ubigeo | depart | distrito | pob_habitantes | pob_ranking | idh_coef | idh_ranking | esp_vida_años | esp_vida_ranking | educ_sec_% | educ_sec_ranking | educ_años | educ_ranking | ingreso_percap_mes | ingreso_percap_ranking | región | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6 | 010101 | Amazonas | Chachapoyas | 32589 | 169 | 0.605711 | 169 | 73.297544 | 1082 | 74.699578 | 182 | 9.7582 | 110 | 1086.762736 | 158 | Selva |
| 7 | 010102 | Amazonas | Asuncion | 262 | 1860 | 0.421303 | 762 | 72.349067 | 1172 | 66.666667 | 473 | 5.8662 | 943 | 569.610318 | 853 | Selva |
| 8 | 010103 | Amazonas | Balsas | 1136 | 1525 | 0.294232 | 1437 | 68.710568 | 1446 | 30.769231 | 1716 | 4.9866 | 1273 | 362.999904 | 1296 | Selva |
| 9 | 010104 | Amazonas | Cheto | 642 | 1721 | 0.329549 | 1228 | 83.896326 | 152 | 49.019608 | 1177 | 4.2735 | 1561 | 342.569379 | 1361 | Selva |
| 10 | 010105 | Amazonas | Chiliquin | 585 | 1749 | 0.264602 | 1592 | 77.261089 | 684 | 34.615385 | 1612 | 3.304 | 1789 | 308.814375 | 1439 | Selva |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1875 | 250302 | Ucayali | Irazola | 10214 | 455 | 0.378183 | 959 | 85.697522 | 85 | 39.927624 | 1471 | 5.1274 | 1219 | 465.880288 | 1059 | Selva |
| 1876 | 250303 | Ucayali | Curimana | 7722 | 572 | 0.378119 | 961 | 85.109174 | 99 | 40.219378 | 1458 | 5.5175 | 1074 | 444.932792 | 1111 | Selva |
| 1877 | 250304 | Ucayali | Neshuya | 7594 | 576 | 0.437379 | 695 | 82.296773 | 229 | 45.68082 | 1302 | 6.1251 | 868 | 614.015288 | 766 | Selva |
| 1878 | 250305 | Ucayali | Alexander Von Humboldt | 5137 | 775 | 0.423282 | 753 | 83.636475 | 164 | 42.929293 | 1393 | 5.8305 | 957 | 582.998041 | 818 | Selva |
| 1879 | 250401 | Ucayali | Purus | 2860 | 1080 | 0.335455 | 1193 | 66.458846 | 1555 | 20.197044 | 1848 | 7.2707 | 537 | 517.759563 | 941 | Selva |
1874 rows × 16 columns
#Ordenamos las columnas
IDH_2017 = IDH_2017.reindex(columns=[ "ubigeo", "depart", "región", 'distrito', 'pob_habitantes', 'pob_ranking', 'idh_coef', 'idh_ranking', 'esp_vida_años', 'esp_vida_ranking', 'educ_sec_%', 'educ_sec_ranking', 'educ_años', 'educ_ranking', 'ingreso_percap_mes', 'ingreso_percap_ranking'])
IDH_2017
| ubigeo | depart | región | distrito | pob_habitantes | pob_ranking | idh_coef | idh_ranking | esp_vida_años | esp_vida_ranking | educ_sec_% | educ_sec_ranking | educ_años | educ_ranking | ingreso_percap_mes | ingreso_percap_ranking | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6 | 010101 | Amazonas | Selva | Chachapoyas | 32589 | 169 | 0.605711 | 169 | 73.297544 | 1082 | 74.699578 | 182 | 9.7582 | 110 | 1086.762736 | 158 |
| 7 | 010102 | Amazonas | Selva | Asuncion | 262 | 1860 | 0.421303 | 762 | 72.349067 | 1172 | 66.666667 | 473 | 5.8662 | 943 | 569.610318 | 853 |
| 8 | 010103 | Amazonas | Selva | Balsas | 1136 | 1525 | 0.294232 | 1437 | 68.710568 | 1446 | 30.769231 | 1716 | 4.9866 | 1273 | 362.999904 | 1296 |
| 9 | 010104 | Amazonas | Selva | Cheto | 642 | 1721 | 0.329549 | 1228 | 83.896326 | 152 | 49.019608 | 1177 | 4.2735 | 1561 | 342.569379 | 1361 |
| 10 | 010105 | Amazonas | Selva | Chiliquin | 585 | 1749 | 0.264602 | 1592 | 77.261089 | 684 | 34.615385 | 1612 | 3.304 | 1789 | 308.814375 | 1439 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1875 | 250302 | Ucayali | Selva | Irazola | 10214 | 455 | 0.378183 | 959 | 85.697522 | 85 | 39.927624 | 1471 | 5.1274 | 1219 | 465.880288 | 1059 |
| 1876 | 250303 | Ucayali | Selva | Curimana | 7722 | 572 | 0.378119 | 961 | 85.109174 | 99 | 40.219378 | 1458 | 5.5175 | 1074 | 444.932792 | 1111 |
| 1877 | 250304 | Ucayali | Selva | Neshuya | 7594 | 576 | 0.437379 | 695 | 82.296773 | 229 | 45.68082 | 1302 | 6.1251 | 868 | 614.015288 | 766 |
| 1878 | 250305 | Ucayali | Selva | Alexander Von Humboldt | 5137 | 775 | 0.423282 | 753 | 83.636475 | 164 | 42.929293 | 1393 | 5.8305 | 957 | 582.998041 | 818 |
| 1879 | 250401 | Ucayali | Selva | Purus | 2860 | 1080 | 0.335455 | 1193 | 66.458846 | 1555 | 20.197044 | 1848 | 7.2707 | 537 | 517.759563 | 941 |
1874 rows × 16 columns
Los gráficos de dispersión son representados a nivel nacional y también por regiones
import altair as alt
Representa la relación entre el coeficiente del idh y el porcentaje depoblación con educación secundaria completa (población 18 años).
#A nivel nacional
alt.Chart(IDH_2017).mark_point(filled = True).encode(
x = alt.X("educ_sec_%:Q"),
y = alt.Y("idh_coef:Q"),
tooltip = ["distrito", "depart", "idh_coef", "ingreso_percap_mes"],
color = alt.Color("región",
legend=alt.Legend( titleOrient='left')),
).properties(width=450, height=450).interactive()
#Por regiones
alt.Chart(IDH_2017).mark_point(filled = True).encode(
x = alt.X("educ_sec_%:Q"),
y = alt.Y("idh_coef:Q"),
tooltip = ["distrito", "depart", "idh_coef", "educ_sec_%"],
color = alt.Color("región",
legend=alt.Legend(orient='bottom', titleOrient='left')),
column = alt.Column("región")
).properties(width=350, height=350).interactive()
Representa la relación entre el coeficiente del idh y la esperanza de vida al nacer (en años).
#A nivel nacional
alt.Chart(IDH_2017).mark_point(filled = True).encode(
x = alt.X("esp_vida_años:Q"),
y = alt.Y("idh_coef:Q"),
tooltip = ["distrito", "depart", "idh_coef", "esp_vida_años"],
color = alt.Color("región",
legend=alt.Legend( titleOrient='left')),
).properties(width=450, height=450).interactive()
#Por regiones
alt.Chart(IDH_2017).mark_point(filled = True).encode(
x = alt.X("esp_vida_años:Q"),
y = alt.Y("idh_coef:Q"),
tooltip = ["distrito", "depart", "idh_coef", "esp_vida_años"],
color = alt.Color("región",
legend=alt.Legend(orient='bottom', titleOrient='left')),
column = alt.Column("región")
).properties(width=300, height=300).interactive()
Representa la relación entre el coeficiente del idh y años de educación (población de 25 años a más).
#A nivel nacional
alt.Chart(IDH_2017).mark_point(filled = True).encode(
x = alt.X("educ_años:Q"),
y = alt.Y("idh_coef:Q"),
tooltip = ["distrito", "depart", "idh_coef", "educ_años"],
color = alt.Color("región",
legend=alt.Legend( titleOrient='left')),
).properties(width=450, height=450).interactive()
# Por regiones
alt.Chart(IDH_2017).mark_point(filled = True).encode(
x = alt.X("educ_años:Q"),
y = alt.Y("idh_coef:Q"),
tooltip = ["distrito", "depart", "idh_coef", "educ_años"],
color = alt.Color("región",
legend=alt.Legend(orient='bottom', titleOrient='left')),
column = alt.Column("región")
).properties(width=300, height=300).interactive()
Representa la relación entre el coeficiente del idh y el ingreso familiar per cápita.
#A nivel nacional
alt.Chart(IDH_2017).mark_point(filled = True).encode(
x = alt.X("ingreso_percap_mes:Q"),
y = alt.Y("idh_coef:Q"),
tooltip = ["distrito", "depart", "idh_coef", "ingreso_percap_mes"],
color = alt.Color("región",
legend=alt.Legend( titleOrient='left')),
).properties(width=450, height=450).interactive()
#Por regiones
alt.Chart(IDH_2017).mark_point(filled = True).encode(
x = alt.X("ingreso_percap_mes:Q"),
y = alt.Y("idh_coef:Q"),
tooltip = ["distrito", "depart", "idh_coef", "ingreso_percap_mes"],
color = alt.Color("región",
legend=alt.Legend(orient='bottom', titleOrient='left')),
column = alt.Column("región")
).properties(width=300, height=300).interactive()
#Generamos un cuadro con los IDH distritales más bajos por departamento
IDH_2017['departamento']= IDH_2017['depart']
IDH_2017_MIN= IDH_2017[['idh_coef', 'departamento', 'depart', 'región']].groupby('depart').min()
IDH_2017_MIN
| idh_coef | departamento | región | |
|---|---|---|---|
| depart | |||
| Amazonas | 0.188044 | Amazonas | Selva |
| Ancash | 0.133801 | Ancash | Costa |
| Apurímac | 0.140808 | Apurímac | Sierra |
| Arequipa | 0.178236 | Arequipa | Costa |
| Ayacucho | 0.109379 | Ayacucho | Sierra |
| Cajamarca | 0.109321 | Cajamarca | Sierra |
| Callao | 0.612243 | Callao | Lima y Callao |
| Cuzco | 0.154525 | Cuzco | Sierra |
| Huancavelica | 0.118631 | Huancavelica | Sierra |
| Huánuco | 0.196013 | Huánuco | Sierra |
| Ica | 0.341821 | Ica | Costa |
| Junín | 0.145551 | Junín | Sierra |
| La Libertad | 0.099556 | La Libertad | Costa |
| Lambayeque | 0.216722 | Lambayeque | Costa |
| Lima | 0.25366 | Lima | Lima y Callao |
| Loreto | 0.200405 | Loreto | Selva |
| Madre de Dios | 0.342773 | Madre de Dios | Selva |
| Moquegua | 0.427346 | Moquegua | Sierra |
| Pasco | 0.3005 | Pasco | Sierra |
| Piura | 0.117568 | Piura | Costa |
| Puno | 0.165481 | Puno | Sierra |
| San Martín | 0.272094 | San Martín | Selva |
| Tacna | 0.31703 | Tacna | Costa |
| Tumbes | 0.372998 | Tumbes | Costa |
| Ucayali | 0.210378 | Ucayali | Selva |
Representa los idh distritales más bajos de cada departamento.
alt.Chart(IDH_2017_MIN).mark_bar().encode(
alt.Y('idh_coef'),
alt.X("departamento"),
tooltip = ["departamento", "idh_coef"],
color = alt.Color("región"),
)
#Generamos un cuadro con los IDH distritales más altos por departamento
IDH_2017_MAX= IDH_2017[['idh_coef', 'departamento', 'región', 'depart']].groupby('depart').max()
IDH_2017_MAX
| idh_coef | departamento | región | |
|---|---|---|---|
| depart | |||
| Amazonas | 0.605711 | Amazonas | Selva |
| Ancash | 0.590474 | Ancash | Costa |
| Apurímac | 0.561174 | Apurímac | Sierra |
| Arequipa | 0.720898 | Arequipa | Costa |
| Ayacucho | 0.512953 | Ayacucho | Sierra |
| Cajamarca | 0.545899 | Cajamarca | Sierra |
| Callao | 0.731345 | Callao | Lima y Callao |
| Cuzco | 0.716253 | Cuzco | Sierra |
| Huancavelica | 0.518108 | Huancavelica | Sierra |
| Huánuco | 0.566558 | Huánuco | Sierra |
| Ica | 0.679034 | Ica | Costa |
| Junín | 0.696607 | Junín | Sierra |
| La Libertad | 0.666722 | La Libertad | Costa |
| Lambayeque | 0.612656 | Lambayeque | Costa |
| Lima | 0.836181 | Lima | Lima y Callao |
| Loreto | 0.619237 | Loreto | Selva |
| Madre de Dios | 0.660495 | Madre de Dios | Selva |
| Moquegua | 0.700733 | Moquegua | Sierra |
| Pasco | 0.595791 | Pasco | Sierra |
| Piura | 0.613133 | Piura | Costa |
| Puno | 0.569807 | Puno | Sierra |
| San Martín | 0.622948 | San Martín | Selva |
| Tacna | 0.69419 | Tacna | Costa |
| Tumbes | 0.591613 | Tumbes | Costa |
| Ucayali | 0.538126 | Ucayali | Selva |
Representa los idh distritales más altos de cada departamento.
alt.Chart(IDH_2017_MAX).mark_bar().encode(
alt.Y("idh_coef"),
alt.X("departamento"),
tooltip = ["departamento", "idh_coef"],
color = alt.Color("región")
)
idh_gen = pd.read_excel("./IDH_PNUD_2017.xlsx", sheet_name="Ind. desigualdad de género")
idh_gen
| PERÚ: Indicadores del Índice de Desigualdad de Género referidos a participación política, empleo y educación según departamento, 2017 | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 | Unnamed: 6 | Unnamed: 7 | Unnamed: 8 | Unnamed: 9 | Unnamed: 10 | Unnamed: 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Departamento | Salud reproductiva 1/ | NaN | Empoderamiento | NaN | NaN | NaN | Mercado laboral | NaN | Número de escaños en el Parlamento nacional | NaN | NaN |
| 1 | NaN | Número de Muertes maternas 1/ | Adolescentes madres o embarazadas | Número de escaños en el Parlamento nacional (%) | NaN | Población con al menos educación secundaria | NaN | Tasa de participación en la fuerza de trabajo | NaN | NaN | NaN | NaN |
| 2 | NaN | (Según procedencia) | % mujeres | 2016-2021 | NaN | (% de 25 y más años de edad) | NaN | (% de 15 y más años de edad) | NaN | 2016-2021 | NaN | NaN |
| 3 | NaN | NaN | (15-19 años) | Mujeres | Hombres | Mujeres | Hombres | Mujeres | Hombres | Mujeres | Hombres | Total |
| 4 | Nacional | 375 | 13,4 | 28,5 | 71,5 | 63,3 | 74,1 | 65,0 | 82,6 | 37 | 93 | 130 |
| 5 | Amazonas | 13 | 23,8 | 50,0 | 50,0 | 41,9 | 53,0 | 72,9 | 88,6 | 1 | 1 | 2 |
| 6 | Áncash | 12 | 13 | 60,0 | 40,0 | 53,2 | 66,8 | 65,9 | 84,7 | 3 | 2 | 5 |
| 7 | Apurímac | 2 | 12 | 0,0 | 100,0 | 41,2 | 62,1 | 75,8 | 88,9 | 0 | 2 | 2 |
| 8 | Arequipa | 6 | 6,6 | 33,3 | 66,7 | 71,6 | 83,4 | 60,9 | 81,0 | 2 | 4 | 6 |
| 9 | Ayacucho | 8 | 16,8 | 33,3 | 66,7 | 42,4 | 63,8 | 70,7 | 83,2 | 1 | 2 | 3 |
| 10 | Cajamarca | 33 | 18,5 | 0,0 | 100,0 | 31,5 | 46,0 | 74,3 | 88,1 | 0 | 6 | 6 |
| 11 | Prov. Const. del Callao | 7 | 10,2 | 50,0 | 50,0 | 80,1 | 89,1 | 59,3 | 82,7 | 2 | 2 | 4 |
| 12 | Cusco | 26 | 9,2 | 20,0 | 80,0 | 48,8 | 66,3 | 75,9 | 84,2 | 1 | 4 | 5 |
| 13 | Huancavelica | 13 | 15,1 | 0,0 | 100,0 | 32,1 | 57,9 | 82,9 | 86,8 | 0 | 2 | 2 |
| 14 | Huánuco | 17 | 16,4 | 33,3 | 66,7 | 39,8 | 52,1 | 68,8 | 86,3 | 1 | 2 | 3 |
| 15 | Ica | 5 | 14,3 | 25,0 | 75,0 | 77,5 | 86,6 | 60,2 | 80,2 | 1 | 3 | 4 |
| 16 | Junín | 15 | 12,1 | 20,0 | 80,0 | 55,8 | 70,7 | 67,3 | 82,1 | 1 | 4 | 5 |
| 17 | La Libertad | 23 | 16,8 | 28,6 | 71,4 | 58,1 | 65,5 | 62,1 | 82,5 | 2 | 5 | 7 |
| 18 | Lambayeque | 12 | 11,8 | 20,0 | 80,0 | 61,8 | 71,9 | 58,5 | 78,9 | 1 | 4 | 5 |
| 19 | Lima | 36 | 10,2 | 35,0 | 65,0 | 79,8 | 88,0 | 63,4 | 80,3 | 14 | 26 | 40 |
| 20 | Provincia de Lima | NaN | 9,6 | 33,3 | 66,7 | 81,4 | 89,4 | 63,4 | 79,9 | 12 | 24 | 36 |
| 21 | Región Lima 2/ | NaN | 14,9 | 50,0 | 50,0 | 63,4 | 74,2 | 62,6 | 84,5 | 2 | 2 | 4 |
| 22 | Loreto | 40 | 30,4 | 50,0 | 50,0 | 52,9 | 65,2 | 60,4 | 82,3 | 2 | 2 | 4 |
| 23 | Madre de Dios | 3 | 16,9 | 0,0 | 100,0 | 63,5 | 76,2 | 68,8 | 87,1 | 0 | 1 | 1 |
| 24 | Moquegua | 1 | 8,6 | 0,0 | 100,0 | 69,3 | 81,1 | 64,7 | 82,8 | 0 | 2 | 2 |
| 25 | Pasco | 8 | 12,6 | 0,0 | 100,0 | 56,3 | 68,7 | 67,4 | 85,3 | 0 | 2 | 2 |
| 26 | Piura | 35 | 14,6 | 42,9 | 57,1 | 52,3 | 63,3 | 57,7 | 81,9 | 3 | 4 | 7 |
| 27 | Puno | 26 | 11,3 | 0,0 | 100,0 | 45,3 | 72,0 | 73,2 | 84,1 | 0 | 5 | 5 |
| 28 | San Martín | 18 | 20 | 25,0 | 75,0 | 44,6 | 50,6 | 63,8 | 89,6 | 1 | 3 | 4 |
| 29 | Tacna | 3 | 9 | 0,0 | 100,0 | 67,0 | 82,9 | 66,8 | 74,8 | 0 | 2 | 2 |
| 30 | Tumbes | 2 | 16 | 50,0 | 50,0 | 67,3 | 71,0 | 63,3 | 86,5 | 1 | 1 | 2 |
| 31 | Ucayali | 11 | 23,1 | 0,0 | 100,0 | 59,6 | 70,8 | 64,7 | 86,9 | 0 | 2 | 2 |
| 32 | Fuente: INEI. Censo de Población y Vivienda 20... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 33 | 1/ Ministerio de Salud. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 34 | Elaboración: PNUD / Unidad del Informe sobre D... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
#Eliminamos las filas
idh_gen = idh_gen.drop([0, 1, 2, 3, 32, 33, 34])
#Cambiamos los encabezados
nuev_enca = ['departamento', 'num_muertes_maternas', 'adolescentes_madres_embarazadas_%',
'muj-escaños_parl_nac_%', 'hom-escaños_parl_nac_%','muj-educ_sec_%',
'hom-educ_sec_%', 'muj_trabajo_%', 'hom_trabajo_%',
'muj-escaños_parl_nac_num', 'hom-escaños_parl_nac_num', 'tot-escaños_parl_nac_num']
idh_gen = idh_gen.rename(columns=dict(zip(idh_gen.columns, nuev_enca)))
idh_gen
| departamento | num_muertes_maternas | adolescentes_madres_embarazadas_% | muj-escaños_parl_nac_% | hom-escaños_parl_nac_% | muj-educ_sec_% | hom-educ_sec_% | muj_trabajo_% | hom_trabajo_% | muj-escaños_parl_nac_num | hom-escaños_parl_nac_num | tot-escaños_parl_nac_num | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | Nacional | 375 | 13,4 | 28,5 | 71,5 | 63,3 | 74,1 | 65,0 | 82,6 | 37 | 93 | 130 |
| 5 | Amazonas | 13 | 23,8 | 50,0 | 50,0 | 41,9 | 53,0 | 72,9 | 88,6 | 1 | 1 | 2 |
| 6 | Áncash | 12 | 13 | 60,0 | 40,0 | 53,2 | 66,8 | 65,9 | 84,7 | 3 | 2 | 5 |
| 7 | Apurímac | 2 | 12 | 0,0 | 100,0 | 41,2 | 62,1 | 75,8 | 88,9 | 0 | 2 | 2 |
| 8 | Arequipa | 6 | 6,6 | 33,3 | 66,7 | 71,6 | 83,4 | 60,9 | 81,0 | 2 | 4 | 6 |
| 9 | Ayacucho | 8 | 16,8 | 33,3 | 66,7 | 42,4 | 63,8 | 70,7 | 83,2 | 1 | 2 | 3 |
| 10 | Cajamarca | 33 | 18,5 | 0,0 | 100,0 | 31,5 | 46,0 | 74,3 | 88,1 | 0 | 6 | 6 |
| 11 | Prov. Const. del Callao | 7 | 10,2 | 50,0 | 50,0 | 80,1 | 89,1 | 59,3 | 82,7 | 2 | 2 | 4 |
| 12 | Cusco | 26 | 9,2 | 20,0 | 80,0 | 48,8 | 66,3 | 75,9 | 84,2 | 1 | 4 | 5 |
| 13 | Huancavelica | 13 | 15,1 | 0,0 | 100,0 | 32,1 | 57,9 | 82,9 | 86,8 | 0 | 2 | 2 |
| 14 | Huánuco | 17 | 16,4 | 33,3 | 66,7 | 39,8 | 52,1 | 68,8 | 86,3 | 1 | 2 | 3 |
| 15 | Ica | 5 | 14,3 | 25,0 | 75,0 | 77,5 | 86,6 | 60,2 | 80,2 | 1 | 3 | 4 |
| 16 | Junín | 15 | 12,1 | 20,0 | 80,0 | 55,8 | 70,7 | 67,3 | 82,1 | 1 | 4 | 5 |
| 17 | La Libertad | 23 | 16,8 | 28,6 | 71,4 | 58,1 | 65,5 | 62,1 | 82,5 | 2 | 5 | 7 |
| 18 | Lambayeque | 12 | 11,8 | 20,0 | 80,0 | 61,8 | 71,9 | 58,5 | 78,9 | 1 | 4 | 5 |
| 19 | Lima | 36 | 10,2 | 35,0 | 65,0 | 79,8 | 88,0 | 63,4 | 80,3 | 14 | 26 | 40 |
| 20 | Provincia de Lima | NaN | 9,6 | 33,3 | 66,7 | 81,4 | 89,4 | 63,4 | 79,9 | 12 | 24 | 36 |
| 21 | Región Lima 2/ | NaN | 14,9 | 50,0 | 50,0 | 63,4 | 74,2 | 62,6 | 84,5 | 2 | 2 | 4 |
| 22 | Loreto | 40 | 30,4 | 50,0 | 50,0 | 52,9 | 65,2 | 60,4 | 82,3 | 2 | 2 | 4 |
| 23 | Madre de Dios | 3 | 16,9 | 0,0 | 100,0 | 63,5 | 76,2 | 68,8 | 87,1 | 0 | 1 | 1 |
| 24 | Moquegua | 1 | 8,6 | 0,0 | 100,0 | 69,3 | 81,1 | 64,7 | 82,8 | 0 | 2 | 2 |
| 25 | Pasco | 8 | 12,6 | 0,0 | 100,0 | 56,3 | 68,7 | 67,4 | 85,3 | 0 | 2 | 2 |
| 26 | Piura | 35 | 14,6 | 42,9 | 57,1 | 52,3 | 63,3 | 57,7 | 81,9 | 3 | 4 | 7 |
| 27 | Puno | 26 | 11,3 | 0,0 | 100,0 | 45,3 | 72,0 | 73,2 | 84,1 | 0 | 5 | 5 |
| 28 | San Martín | 18 | 20 | 25,0 | 75,0 | 44,6 | 50,6 | 63,8 | 89,6 | 1 | 3 | 4 |
| 29 | Tacna | 3 | 9 | 0,0 | 100,0 | 67,0 | 82,9 | 66,8 | 74,8 | 0 | 2 | 2 |
| 30 | Tumbes | 2 | 16 | 50,0 | 50,0 | 67,3 | 71,0 | 63,3 | 86,5 | 1 | 1 | 2 |
| 31 | Ucayali | 11 | 23,1 | 0,0 | 100,0 | 59,6 | 70,8 | 64,7 | 86,9 | 0 | 2 | 2 |
#Dado que las observaciones no se encuentran en formato numérico, cambiamos las comas por puntos como indicador de decimal
idh_gen['muj-educ_sec_%']= idh_gen['muj-educ_sec_%'].str.replace(",", ".")
idh_gen['hom-educ_sec_%']= idh_gen['hom-educ_sec_%'].str.replace(",", ".")
idh_gen['muj_trabajo_%']= idh_gen['muj_trabajo_%'].str.replace(",", ".")
idh_gen['hom_trabajo_%']= idh_gen['hom_trabajo_%'].str.replace(",", ".")
idh_gen
| departamento | num_muertes_maternas | adolescentes_madres_embarazadas_% | muj-escaños_parl_nac_% | hom-escaños_parl_nac_% | muj-educ_sec_% | hom-educ_sec_% | muj_trabajo_% | hom_trabajo_% | muj-escaños_parl_nac_num | hom-escaños_parl_nac_num | tot-escaños_parl_nac_num | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | Nacional | 375 | 13,4 | 28,5 | 71,5 | 63.3 | 74.1 | 65.0 | 82.6 | 37 | 93 | 130 |
| 5 | Amazonas | 13 | 23,8 | 50,0 | 50,0 | 41.9 | 53.0 | 72.9 | 88.6 | 1 | 1 | 2 |
| 6 | Áncash | 12 | 13 | 60,0 | 40,0 | 53.2 | 66.8 | 65.9 | 84.7 | 3 | 2 | 5 |
| 7 | Apurímac | 2 | 12 | 0,0 | 100,0 | 41.2 | 62.1 | 75.8 | 88.9 | 0 | 2 | 2 |
| 8 | Arequipa | 6 | 6,6 | 33,3 | 66,7 | 71.6 | 83.4 | 60.9 | 81.0 | 2 | 4 | 6 |
| 9 | Ayacucho | 8 | 16,8 | 33,3 | 66,7 | 42.4 | 63.8 | 70.7 | 83.2 | 1 | 2 | 3 |
| 10 | Cajamarca | 33 | 18,5 | 0,0 | 100,0 | 31.5 | 46.0 | 74.3 | 88.1 | 0 | 6 | 6 |
| 11 | Prov. Const. del Callao | 7 | 10,2 | 50,0 | 50,0 | 80.1 | 89.1 | 59.3 | 82.7 | 2 | 2 | 4 |
| 12 | Cusco | 26 | 9,2 | 20,0 | 80,0 | 48.8 | 66.3 | 75.9 | 84.2 | 1 | 4 | 5 |
| 13 | Huancavelica | 13 | 15,1 | 0,0 | 100,0 | 32.1 | 57.9 | 82.9 | 86.8 | 0 | 2 | 2 |
| 14 | Huánuco | 17 | 16,4 | 33,3 | 66,7 | 39.8 | 52.1 | 68.8 | 86.3 | 1 | 2 | 3 |
| 15 | Ica | 5 | 14,3 | 25,0 | 75,0 | 77.5 | 86.6 | 60.2 | 80.2 | 1 | 3 | 4 |
| 16 | Junín | 15 | 12,1 | 20,0 | 80,0 | 55.8 | 70.7 | 67.3 | 82.1 | 1 | 4 | 5 |
| 17 | La Libertad | 23 | 16,8 | 28,6 | 71,4 | 58.1 | 65.5 | 62.1 | 82.5 | 2 | 5 | 7 |
| 18 | Lambayeque | 12 | 11,8 | 20,0 | 80,0 | 61.8 | 71.9 | 58.5 | 78.9 | 1 | 4 | 5 |
| 19 | Lima | 36 | 10,2 | 35,0 | 65,0 | 79.8 | 88.0 | 63.4 | 80.3 | 14 | 26 | 40 |
| 20 | Provincia de Lima | NaN | 9,6 | 33,3 | 66,7 | 81.4 | 89.4 | 63.4 | 79.9 | 12 | 24 | 36 |
| 21 | Región Lima 2/ | NaN | 14,9 | 50,0 | 50,0 | 63.4 | 74.2 | 62.6 | 84.5 | 2 | 2 | 4 |
| 22 | Loreto | 40 | 30,4 | 50,0 | 50,0 | 52.9 | 65.2 | 60.4 | 82.3 | 2 | 2 | 4 |
| 23 | Madre de Dios | 3 | 16,9 | 0,0 | 100,0 | 63.5 | 76.2 | 68.8 | 87.1 | 0 | 1 | 1 |
| 24 | Moquegua | 1 | 8,6 | 0,0 | 100,0 | 69.3 | 81.1 | 64.7 | 82.8 | 0 | 2 | 2 |
| 25 | Pasco | 8 | 12,6 | 0,0 | 100,0 | 56.3 | 68.7 | 67.4 | 85.3 | 0 | 2 | 2 |
| 26 | Piura | 35 | 14,6 | 42,9 | 57,1 | 52.3 | 63.3 | 57.7 | 81.9 | 3 | 4 | 7 |
| 27 | Puno | 26 | 11,3 | 0,0 | 100,0 | 45.3 | 72.0 | 73.2 | 84.1 | 0 | 5 | 5 |
| 28 | San Martín | 18 | 20 | 25,0 | 75,0 | 44.6 | 50.6 | 63.8 | 89.6 | 1 | 3 | 4 |
| 29 | Tacna | 3 | 9 | 0,0 | 100,0 | 67.0 | 82.9 | 66.8 | 74.8 | 0 | 2 | 2 |
| 30 | Tumbes | 2 | 16 | 50,0 | 50,0 | 67.3 | 71.0 | 63.3 | 86.5 | 1 | 1 | 2 |
| 31 | Ucayali | 11 | 23,1 | 0,0 | 100,0 | 59.6 | 70.8 | 64.7 | 86.9 | 0 | 2 | 2 |
#Cambiamos el tipo de las observaciones
idh_gen['muj_trabajo_%'] = idh_gen['muj_trabajo_%'].astype("float")
idh_gen['hom_trabajo_%'] = idh_gen['hom_trabajo_%'].astype("float")
idh_gen['hom-educ_sec_%'] = idh_gen['hom-educ_sec_%'].astype("float")
idh_gen['muj-educ_sec_%'] = idh_gen['muj-educ_sec_%'].astype("float")
idh_gen
| departamento | num_muertes_maternas | adolescentes_madres_embarazadas_% | muj-escaños_parl_nac_% | hom-escaños_parl_nac_% | muj-educ_sec_% | hom-educ_sec_% | muj_trabajo_% | hom_trabajo_% | muj-escaños_parl_nac_num | hom-escaños_parl_nac_num | tot-escaños_parl_nac_num | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | Nacional | 375 | 13,4 | 28,5 | 71,5 | 63.3 | 74.1 | 65.0 | 82.6 | 37 | 93 | 130 |
| 5 | Amazonas | 13 | 23,8 | 50,0 | 50,0 | 41.9 | 53.0 | 72.9 | 88.6 | 1 | 1 | 2 |
| 6 | Áncash | 12 | 13 | 60,0 | 40,0 | 53.2 | 66.8 | 65.9 | 84.7 | 3 | 2 | 5 |
| 7 | Apurímac | 2 | 12 | 0,0 | 100,0 | 41.2 | 62.1 | 75.8 | 88.9 | 0 | 2 | 2 |
| 8 | Arequipa | 6 | 6,6 | 33,3 | 66,7 | 71.6 | 83.4 | 60.9 | 81.0 | 2 | 4 | 6 |
| 9 | Ayacucho | 8 | 16,8 | 33,3 | 66,7 | 42.4 | 63.8 | 70.7 | 83.2 | 1 | 2 | 3 |
| 10 | Cajamarca | 33 | 18,5 | 0,0 | 100,0 | 31.5 | 46.0 | 74.3 | 88.1 | 0 | 6 | 6 |
| 11 | Prov. Const. del Callao | 7 | 10,2 | 50,0 | 50,0 | 80.1 | 89.1 | 59.3 | 82.7 | 2 | 2 | 4 |
| 12 | Cusco | 26 | 9,2 | 20,0 | 80,0 | 48.8 | 66.3 | 75.9 | 84.2 | 1 | 4 | 5 |
| 13 | Huancavelica | 13 | 15,1 | 0,0 | 100,0 | 32.1 | 57.9 | 82.9 | 86.8 | 0 | 2 | 2 |
| 14 | Huánuco | 17 | 16,4 | 33,3 | 66,7 | 39.8 | 52.1 | 68.8 | 86.3 | 1 | 2 | 3 |
| 15 | Ica | 5 | 14,3 | 25,0 | 75,0 | 77.5 | 86.6 | 60.2 | 80.2 | 1 | 3 | 4 |
| 16 | Junín | 15 | 12,1 | 20,0 | 80,0 | 55.8 | 70.7 | 67.3 | 82.1 | 1 | 4 | 5 |
| 17 | La Libertad | 23 | 16,8 | 28,6 | 71,4 | 58.1 | 65.5 | 62.1 | 82.5 | 2 | 5 | 7 |
| 18 | Lambayeque | 12 | 11,8 | 20,0 | 80,0 | 61.8 | 71.9 | 58.5 | 78.9 | 1 | 4 | 5 |
| 19 | Lima | 36 | 10,2 | 35,0 | 65,0 | 79.8 | 88.0 | 63.4 | 80.3 | 14 | 26 | 40 |
| 20 | Provincia de Lima | NaN | 9,6 | 33,3 | 66,7 | 81.4 | 89.4 | 63.4 | 79.9 | 12 | 24 | 36 |
| 21 | Región Lima 2/ | NaN | 14,9 | 50,0 | 50,0 | 63.4 | 74.2 | 62.6 | 84.5 | 2 | 2 | 4 |
| 22 | Loreto | 40 | 30,4 | 50,0 | 50,0 | 52.9 | 65.2 | 60.4 | 82.3 | 2 | 2 | 4 |
| 23 | Madre de Dios | 3 | 16,9 | 0,0 | 100,0 | 63.5 | 76.2 | 68.8 | 87.1 | 0 | 1 | 1 |
| 24 | Moquegua | 1 | 8,6 | 0,0 | 100,0 | 69.3 | 81.1 | 64.7 | 82.8 | 0 | 2 | 2 |
| 25 | Pasco | 8 | 12,6 | 0,0 | 100,0 | 56.3 | 68.7 | 67.4 | 85.3 | 0 | 2 | 2 |
| 26 | Piura | 35 | 14,6 | 42,9 | 57,1 | 52.3 | 63.3 | 57.7 | 81.9 | 3 | 4 | 7 |
| 27 | Puno | 26 | 11,3 | 0,0 | 100,0 | 45.3 | 72.0 | 73.2 | 84.1 | 0 | 5 | 5 |
| 28 | San Martín | 18 | 20 | 25,0 | 75,0 | 44.6 | 50.6 | 63.8 | 89.6 | 1 | 3 | 4 |
| 29 | Tacna | 3 | 9 | 0,0 | 100,0 | 67.0 | 82.9 | 66.8 | 74.8 | 0 | 2 | 2 |
| 30 | Tumbes | 2 | 16 | 50,0 | 50,0 | 67.3 | 71.0 | 63.3 | 86.5 | 1 | 1 | 2 |
| 31 | Ucayali | 11 | 23,1 | 0,0 | 100,0 | 59.6 | 70.8 | 64.7 | 86.9 | 0 | 2 | 2 |
Representa la comparación entre el porcentaje de mujeres y hombres con al menos educación secundaria (de 25 años de edad a más).
mujeres_trab= alt.Chart(idh_gen).mark_bar().encode(
alt.Y( "departamento"),
alt.X("muj_trabajo_%"),
color= alt.value ('plum'),
tooltip= ['muj_trabajo_%'])
mujeres_trab
hombres_trab= alt.Chart(idh_gen).mark_bar().encode(
y = "departamento",
x = "hom_trabajo_%",
color= alt.value('powderblue'),
tooltip= ['hom_trabajo_%'])
hombres_trab
comparacion_trabajo = hombres_trab + mujeres_trab
comparacion_trabajo
Representa la comparación entre el porcentaje de mujeres y hombres que participan en la fuerza de trabajo (de 15 años de edad a más).
mujeres_educ= alt.Chart(idh_gen).mark_bar().encode(
alt.Y( "departamento"),
alt.X('muj-educ_sec_%'),
color= alt.value ('rosybrown'),
tooltip= ['muj-educ_sec_%'])
mujeres_educ
hombres_educ= alt.Chart(idh_gen).mark_bar().encode(
y = "departamento",
x = "hom-educ_sec_%",
color= alt.value('moccasin'),
tooltip= ['hom-educ_sec_%'])
hombres_educ
comparacion_educacion = hombres_educ + mujeres_educ
comparacion_educacion
#generamos las columnas de las gaps
idh_gen['gap_educ_%']= idh_gen['hom-educ_sec_%'] - idh_gen['muj-educ_sec_%']
idh_gen['gap_trab_%']= idh_gen['hom_trabajo_%'] - idh_gen['muj_trabajo_%']
idh_gen
| departamento | num_muertes_maternas | adolescentes_madres_embarazadas_% | muj-escaños_parl_nac_% | hom-escaños_parl_nac_% | muj-educ_sec_% | hom-educ_sec_% | muj_trabajo_% | hom_trabajo_% | muj-escaños_parl_nac_num | hom-escaños_parl_nac_num | tot-escaños_parl_nac_num | gap_educ_% | gap_trab_% | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | Nacional | 375 | 13,4 | 28,5 | 71,5 | 63.3 | 74.1 | 65.0 | 82.6 | 37 | 93 | 130 | 10.8 | 17.6 |
| 5 | Amazonas | 13 | 23,8 | 50,0 | 50,0 | 41.9 | 53.0 | 72.9 | 88.6 | 1 | 1 | 2 | 11.1 | 15.7 |
| 6 | Áncash | 12 | 13 | 60,0 | 40,0 | 53.2 | 66.8 | 65.9 | 84.7 | 3 | 2 | 5 | 13.6 | 18.8 |
| 7 | Apurímac | 2 | 12 | 0,0 | 100,0 | 41.2 | 62.1 | 75.8 | 88.9 | 0 | 2 | 2 | 20.9 | 13.1 |
| 8 | Arequipa | 6 | 6,6 | 33,3 | 66,7 | 71.6 | 83.4 | 60.9 | 81.0 | 2 | 4 | 6 | 11.8 | 20.1 |
| 9 | Ayacucho | 8 | 16,8 | 33,3 | 66,7 | 42.4 | 63.8 | 70.7 | 83.2 | 1 | 2 | 3 | 21.4 | 12.5 |
| 10 | Cajamarca | 33 | 18,5 | 0,0 | 100,0 | 31.5 | 46.0 | 74.3 | 88.1 | 0 | 6 | 6 | 14.5 | 13.8 |
| 11 | Prov. Const. del Callao | 7 | 10,2 | 50,0 | 50,0 | 80.1 | 89.1 | 59.3 | 82.7 | 2 | 2 | 4 | 9.0 | 23.4 |
| 12 | Cusco | 26 | 9,2 | 20,0 | 80,0 | 48.8 | 66.3 | 75.9 | 84.2 | 1 | 4 | 5 | 17.5 | 8.3 |
| 13 | Huancavelica | 13 | 15,1 | 0,0 | 100,0 | 32.1 | 57.9 | 82.9 | 86.8 | 0 | 2 | 2 | 25.8 | 3.9 |
| 14 | Huánuco | 17 | 16,4 | 33,3 | 66,7 | 39.8 | 52.1 | 68.8 | 86.3 | 1 | 2 | 3 | 12.3 | 17.5 |
| 15 | Ica | 5 | 14,3 | 25,0 | 75,0 | 77.5 | 86.6 | 60.2 | 80.2 | 1 | 3 | 4 | 9.1 | 20.0 |
| 16 | Junín | 15 | 12,1 | 20,0 | 80,0 | 55.8 | 70.7 | 67.3 | 82.1 | 1 | 4 | 5 | 14.9 | 14.8 |
| 17 | La Libertad | 23 | 16,8 | 28,6 | 71,4 | 58.1 | 65.5 | 62.1 | 82.5 | 2 | 5 | 7 | 7.4 | 20.4 |
| 18 | Lambayeque | 12 | 11,8 | 20,0 | 80,0 | 61.8 | 71.9 | 58.5 | 78.9 | 1 | 4 | 5 | 10.1 | 20.4 |
| 19 | Lima | 36 | 10,2 | 35,0 | 65,0 | 79.8 | 88.0 | 63.4 | 80.3 | 14 | 26 | 40 | 8.2 | 16.9 |
| 20 | Provincia de Lima | NaN | 9,6 | 33,3 | 66,7 | 81.4 | 89.4 | 63.4 | 79.9 | 12 | 24 | 36 | 8.0 | 16.5 |
| 21 | Región Lima 2/ | NaN | 14,9 | 50,0 | 50,0 | 63.4 | 74.2 | 62.6 | 84.5 | 2 | 2 | 4 | 10.8 | 21.9 |
| 22 | Loreto | 40 | 30,4 | 50,0 | 50,0 | 52.9 | 65.2 | 60.4 | 82.3 | 2 | 2 | 4 | 12.3 | 21.9 |
| 23 | Madre de Dios | 3 | 16,9 | 0,0 | 100,0 | 63.5 | 76.2 | 68.8 | 87.1 | 0 | 1 | 1 | 12.7 | 18.3 |
| 24 | Moquegua | 1 | 8,6 | 0,0 | 100,0 | 69.3 | 81.1 | 64.7 | 82.8 | 0 | 2 | 2 | 11.8 | 18.1 |
| 25 | Pasco | 8 | 12,6 | 0,0 | 100,0 | 56.3 | 68.7 | 67.4 | 85.3 | 0 | 2 | 2 | 12.4 | 17.9 |
| 26 | Piura | 35 | 14,6 | 42,9 | 57,1 | 52.3 | 63.3 | 57.7 | 81.9 | 3 | 4 | 7 | 11.0 | 24.2 |
| 27 | Puno | 26 | 11,3 | 0,0 | 100,0 | 45.3 | 72.0 | 73.2 | 84.1 | 0 | 5 | 5 | 26.7 | 10.9 |
| 28 | San Martín | 18 | 20 | 25,0 | 75,0 | 44.6 | 50.6 | 63.8 | 89.6 | 1 | 3 | 4 | 6.0 | 25.8 |
| 29 | Tacna | 3 | 9 | 0,0 | 100,0 | 67.0 | 82.9 | 66.8 | 74.8 | 0 | 2 | 2 | 15.9 | 8.0 |
| 30 | Tumbes | 2 | 16 | 50,0 | 50,0 | 67.3 | 71.0 | 63.3 | 86.5 | 1 | 1 | 2 | 3.7 | 23.2 |
| 31 | Ucayali | 11 | 23,1 | 0,0 | 100,0 | 59.6 | 70.8 | 64.7 | 86.9 | 0 | 2 | 2 | 11.2 | 22.2 |
Representa la brecha de mujeres y hombres con al menos educación secundaria (de 25 años de edad a más) en puntos porcentuales.
alt.Chart(idh_gen).mark_bar().encode(
alt.Y( "gap_educ_%"),
alt.X('departamento'),
color= alt.value ('darkseagreen'),
tooltip= ['gap_educ_%'])
Representa la brecha de mujeres y hombres que participan en la fuerza de trabajo (de 15 años de edad a más) en puntos porcentuales.
alt.Chart(idh_gen).mark_bar().encode(
alt.Y( "gap_trab_%"),
alt.X('departamento'),
color= alt.value ('sandybrown'),
tooltip= ['gap_trab_%'])